To evaluate the performance under a strong dataset shift, we evaluated Mini-ImageNet trained models on tasks sampled from CUB-200-2011. In Table REF  (right), we observe that models are not affected at all by the imbalanced setting despite the harder scenario. In Appendix , we provide additional experiments with BTAML [1]}, and an analysis of the correlation between meta-dataset size and performance
